Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations20,000
Missing cells55,841
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.2 MiB
Average record size in memory1.7 KiB

Variable types

Numeric1
Text9
URL1
Unsupported2
DateTime1

Alerts

comments has 2349 (11.7%) missing values Missing
journal-ref has 9486 (47.4%) missing values Missing
doi has 7396 (37.0%) missing values Missing
report-no has 18106 (90.5%) missing values Missing
license has 18504 (92.5%) missing values Missing
id has unique values Unique
versions is an unsupported type, check if it needs cleaning or further analysis Unsupported
authors_parsed is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-07-03 11:07:12.650355
Analysis finished2025-07-03 11:08:35.436338
Duration1 minute and 22.79 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean706.02589
Minimum704.0001
Maximum708.2148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2025-07-03T16:08:35.471272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum704.0001
5-th percentile704.1001
Q1705.09977
median706.13135
Q3707.18293
95-th percentile708.1148
Maximum708.2148
Range4.2147
Interquartile range (IQR)2.08315

Descriptive statistics

Standard deviation1.282659
Coefficient of variation (CV)0.0018167308
Kurtosis-1.1709981
Mean706.02589
Median Absolute Deviation (MAD)1.0416
Skewness0.021111905
Sum14120518
Variance1.6452141
MonotonicityStrictly increasing
2025-07-03T16:08:35.514464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
704.0001 1
 
< 0.1%
707.016 1
 
< 0.1%
707.0167 1
 
< 0.1%
707.0166 1
 
< 0.1%
707.0165 1
 
< 0.1%
707.0164 1
 
< 0.1%
707.0163 1
 
< 0.1%
707.0162 1
 
< 0.1%
707.0161 1
 
< 0.1%
707.0159 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
ValueCountFrequency (%)
704.0001 1
< 0.1%
704.0002 1
< 0.1%
704.0003 1
< 0.1%
704.0004 1
< 0.1%
704.0005 1
< 0.1%
704.0006 1
< 0.1%
704.0007 1
< 0.1%
704.0008 1
< 0.1%
704.0009 1
< 0.1%
704.001 1
< 0.1%
ValueCountFrequency (%)
708.2148 1
< 0.1%
708.2147 1
< 0.1%
708.2146 1
< 0.1%
708.2145 1
< 0.1%
708.2144 1
< 0.1%
708.2143 1
< 0.1%
708.2142 1
< 0.1%
708.2141 1
< 0.1%
708.214 1
< 0.1%
708.2139 1
< 0.1%
Distinct14553
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-07-03T16:08:35.632228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length41
Mean length14.79595
Min length3

Characters and Unicode

Total characters295,919
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11,068 ?
Unique (%)55.3%

Sample

1st rowPavel Nadolsky
2nd rowLouis Theran
3rd rowHongjun Pan
4th rowDavid Callan
5th rowAlberto Torchinsky
ValueCountFrequency (%)
dr 337
 
0.8%
a 323
 
0.7%
michael 275
 
0.6%
m 273
 
0.6%
david 220
 
0.5%
j 210
 
0.5%
alexander 203
 
0.5%
de 190
 
0.4%
s 185
 
0.4%
thomas 159
 
0.4%
Other values (16446) 42269
94.7%
2025-07-03T16:08:35.801338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 29254
 
9.9%
24675
 
8.3%
e 22834
 
7.7%
i 21459
 
7.3%
n 19240
 
6.5%
r 18175
 
6.1%
o 17141
 
5.8%
l 11354
 
3.8%
s 10072
 
3.4%
t 9127
 
3.1%
Other values (63) 112588
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 295919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 29254
 
9.9%
24675
 
8.3%
e 22834
 
7.7%
i 21459
 
7.3%
n 19240
 
6.5%
r 18175
 
6.1%
o 17141
 
5.8%
l 11354
 
3.8%
s 10072
 
3.4%
t 9127
 
3.1%
Other values (63) 112588
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 295919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 29254
 
9.9%
24675
 
8.3%
e 22834
 
7.7%
i 21459
 
7.3%
n 19240
 
6.5%
r 18175
 
6.1%
o 17141
 
5.8%
l 11354
 
3.8%
s 10072
 
3.4%
t 9127
 
3.1%
Other values (63) 112588
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 295919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 29254
 
9.9%
24675
 
8.3%
e 22834
 
7.7%
i 21459
 
7.3%
n 19240
 
6.5%
r 18175
 
6.1%
o 17141
 
5.8%
l 11354
 
3.8%
s 10072
 
3.4%
t 9127
 
3.1%
Other values (63) 112588
38.0%
Distinct18434
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2025-07-03T16:08:35.920645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1,467
Median length792
Mean length55.96375
Min length4

Characters and Unicode

Total characters1,119,275
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17,327 ?
Unique (%)86.6%

Sample

1st rowC. Bal\'azs, E. L. Berger, P. M. Nadolsky, C.-P. Yuan
2nd rowIleana Streinu and Louis Theran
3rd rowHongjun Pan
4th rowDavid Callan
5th rowWael Abu-Shammala and Alberto Torchinsky
ValueCountFrequency (%)
and 7452
 
4.3%
a 4078
 
2.3%
m 3751
 
2.1%
j 3162
 
1.8%
s 2700
 
1.5%
r 2121
 
1.2%
1 2103
 
1.2%
p 1927
 
1.1%
d 1895
 
1.1%
c 1853
 
1.1%
Other values (38170) 143610
82.2%
2025-07-03T16:08:36.094095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161039
 
14.4%
a 84685
 
7.6%
e 64871
 
5.8%
n 60772
 
5.4%
i 59275
 
5.3%
. 49793
 
4.4%
o 49467
 
4.4%
r 49455
 
4.4%
, 41798
 
3.7%
l 30956
 
2.8%
Other values (77) 467164
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1119275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
161039
 
14.4%
a 84685
 
7.6%
e 64871
 
5.8%
n 60772
 
5.4%
i 59275
 
5.3%
. 49793
 
4.4%
o 49467
 
4.4%
r 49455
 
4.4%
, 41798
 
3.7%
l 30956
 
2.8%
Other values (77) 467164
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1119275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
161039
 
14.4%
a 84685
 
7.6%
e 64871
 
5.8%
n 60772
 
5.4%
i 59275
 
5.3%
. 49793
 
4.4%
o 49467
 
4.4%
r 49455
 
4.4%
, 41798
 
3.7%
l 30956
 
2.8%
Other values (77) 467164
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1119275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
161039
 
14.4%
a 84685
 
7.6%
e 64871
 
5.8%
n 60772
 
5.4%
i 59275
 
5.3%
. 49793
 
4.4%
o 49467
 
4.4%
r 49455
 
4.4%
, 41798
 
3.7%
l 30956
 
2.8%
Other values (77) 467164
41.7%

title
Text

Distinct19989
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-07-03T16:08:36.194095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length231
Median length172
Mean length70.51015
Min length8

Characters and Unicode

Total characters1,410,203
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19,983 ?
Unique (%)99.9%

Sample

1st rowCalculation of prompt diphoton production cross sections at Tevatron and LHC energies
2nd rowSparsity-certifying Graph Decompositions
3rd rowThe evolution of the Earth-Moon system based on the dark matter field fluid model
4th rowA determinant of Stirling cycle numbers counts unlabeled acyclic single-source automata
5th rowFrom dyadic $\Lambda_{\alpha}$ to $\Lambda_{\alpha}$
ValueCountFrequency (%)
of 11705
 
6.1%
the 9183
 
4.8%
in 6781
 
3.5%
and 6353
 
3.3%
a 4006
 
2.1%
for 3052
 
1.6%
on 2591
 
1.4%
with 1926
 
1.0%
to 1345
 
0.7%
quantum 1316
 
0.7%
Other values (21516) 143033
74.8%
2025-07-03T16:08:36.349823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179633
12.7%
e 115238
 
8.2%
i 98866
 
7.0%
n 94186
 
6.7%
o 94138
 
6.7%
a 92750
 
6.6%
t 91450
 
6.5%
r 75557
 
5.4%
s 68801
 
4.9%
l 51838
 
3.7%
Other values (86) 447746
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1410203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
179633
12.7%
e 115238
 
8.2%
i 98866
 
7.0%
n 94186
 
6.7%
o 94138
 
6.7%
a 92750
 
6.6%
t 91450
 
6.5%
r 75557
 
5.4%
s 68801
 
4.9%
l 51838
 
3.7%
Other values (86) 447746
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1410203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
179633
12.7%
e 115238
 
8.2%
i 98866
 
7.0%
n 94186
 
6.7%
o 94138
 
6.7%
a 92750
 
6.6%
t 91450
 
6.5%
r 75557
 
5.4%
s 68801
 
4.9%
l 51838
 
3.7%
Other values (86) 447746
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1410203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
179633
12.7%
e 115238
 
8.2%
i 98866
 
7.0%
n 94186
 
6.7%
o 94138
 
6.7%
a 92750
 
6.6%
t 91450
 
6.5%
r 75557
 
5.4%
s 68801
 
4.9%
l 51838
 
3.7%
Other values (86) 447746
31.8%

comments
Text

Missing 

Distinct13002
Distinct (%)73.7%
Missing2349
Missing (%)11.7%
Memory size1.9 MiB
2025-07-03T16:08:36.448790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length408
Median length345
Mean length61.106056
Min length1

Characters and Unicode

Total characters1,078,583
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12,312 ?
Unique (%)69.8%

Sample

1st row37 pages, 15 figures; published version
2nd rowTo appear in Graphs and Combinatorics
3rd row23 pages, 3 figures
4th row11 pages
5th row6 pages, 4 figures, accepted by PRA
ValueCountFrequency (%)
pages 14445
 
8.5%
figures 9705
 
5.7%
in 6074
 
3.6%
to 4406
 
2.6%
the 4245
 
2.5%
4 3158
 
1.8%
of 3096
 
1.8%
and 2986
 
1.7%
accepted 2516
 
1.5%
version 2347
 
1.4%
Other values (9981) 117957
69.0%
2025-07-03T16:08:36.635907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160796
 
14.9%
e 98839
 
9.2%
s 62072
 
5.8%
a 58851
 
5.5%
i 58344
 
5.4%
r 50350
 
4.7%
t 49688
 
4.6%
n 45841
 
4.3%
o 45771
 
4.2%
p 37606
 
3.5%
Other values (84) 410425
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1078583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
160796
 
14.9%
e 98839
 
9.2%
s 62072
 
5.8%
a 58851
 
5.5%
i 58344
 
5.4%
r 50350
 
4.7%
t 49688
 
4.6%
n 45841
 
4.3%
o 45771
 
4.2%
p 37606
 
3.5%
Other values (84) 410425
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1078583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
160796
 
14.9%
e 98839
 
9.2%
s 62072
 
5.8%
a 58851
 
5.5%
i 58344
 
5.4%
r 50350
 
4.7%
t 49688
 
4.6%
n 45841
 
4.3%
o 45771
 
4.2%
p 37606
 
3.5%
Other values (84) 410425
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1078583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
160796
 
14.9%
e 98839
 
9.2%
s 62072
 
5.8%
a 58851
 
5.5%
i 58344
 
5.4%
r 50350
 
4.7%
t 49688
 
4.6%
n 45841
 
4.3%
o 45771
 
4.2%
p 37606
 
3.5%
Other values (84) 410425
38.1%

journal-ref
Text

Missing 

Distinct10458
Distinct (%)99.5%
Missing9486
Missing (%)47.4%
Memory size1.2 MiB
2025-07-03T16:08:36.746287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length237
Median length211
Mean length37.046034
Min length9

Characters and Unicode

Total characters389,502
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10,444 ?
Unique (%)99.3%

Sample

1st rowPhys.Rev.D76:013009,2007
2nd rowIllinois J. Math. 52 (2008) no.2, 681-689
3rd rowPhys.Rev.D76:044016,2007
4th rowJournal of Applied Physics, vol 104, 073536 (2008)
5th rowAstrophys.J.663:1149-1173,2007
ValueCountFrequency (%)
2007 2696
 
5.7%
phys 2158
 
4.6%
rev 1443
 
3.1%
2008 1440
 
3.1%
j 840
 
1.8%
b 767
 
1.6%
76 745
 
1.6%
of 744
 
1.6%
a 615
 
1.3%
lett 563
 
1.2%
Other values (13615) 34884
74.4%
2025-07-03T16:08:36.909606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37194
 
9.5%
0 34035
 
8.7%
. 21469
 
5.5%
2 19865
 
5.1%
7 15370
 
3.9%
1 14044
 
3.6%
e 12985
 
3.3%
s 12621
 
3.2%
, 12274
 
3.2%
o 11013
 
2.8%
Other values (80) 198632
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 389502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
37194
 
9.5%
0 34035
 
8.7%
. 21469
 
5.5%
2 19865
 
5.1%
7 15370
 
3.9%
1 14044
 
3.6%
e 12985
 
3.3%
s 12621
 
3.2%
, 12274
 
3.2%
o 11013
 
2.8%
Other values (80) 198632
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 389502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
37194
 
9.5%
0 34035
 
8.7%
. 21469
 
5.5%
2 19865
 
5.1%
7 15370
 
3.9%
1 14044
 
3.6%
e 12985
 
3.3%
s 12621
 
3.2%
, 12274
 
3.2%
o 11013
 
2.8%
Other values (80) 198632
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 389502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
37194
 
9.5%
0 34035
 
8.7%
. 21469
 
5.5%
2 19865
 
5.1%
7 15370
 
3.9%
1 14044
 
3.6%
e 12985
 
3.3%
s 12621
 
3.2%
, 12274
 
3.2%
o 11013
 
2.8%
Other values (80) 198632
51.0%

doi
Text

Missing 

Distinct12593
Distinct (%)99.9%
Missing7396
Missing (%)37.0%
Memory size1.1 MiB
2025-07-03T16:08:36.998398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length61
Mean length25.188194
Min length10

Characters and Unicode

Total characters317,472
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12,584 ?
Unique (%)99.8%

Sample

1st row10.1103/PhysRevD.76.013009
2nd row10.1103/PhysRevA.75.043613
3rd row10.1103/PhysRevD.76.044016
4th row10.1063/1.2975338
5th row10.1086/518646
ValueCountFrequency (%)
10.1103/physrevlett.99.071302 4
 
< 0.1%
10.1088/0954-3899/35/5/054001 2
 
< 0.1%
10.1111/j.1365-2966.2007.12016.x 2
 
< 0.1%
10.1086/519272 2
 
< 0.1%
10.1016/j.jcp.2007.08.023 2
 
< 0.1%
10.1142/s0218301307008410 2
 
< 0.1%
10.1088/1751-8113/40/40/012 2
 
< 0.1%
10.1088/0953-8984/20/7/075103 2
 
< 0.1%
10.1063/1.2775909 2
 
< 0.1%
10.1086/518714 2
 
< 0.1%
Other values (12649) 12649
99.8%
2025-07-03T16:08:37.137539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58539
18.4%
1 52061
16.4%
. 30054
 
9.5%
/ 18045
 
5.7%
2 16090
 
5.1%
7 15491
 
4.9%
6 14274
 
4.5%
3 14186
 
4.5%
8 11677
 
3.7%
5 9888
 
3.1%
Other values (66) 77167
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 317472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 58539
18.4%
1 52061
16.4%
. 30054
 
9.5%
/ 18045
 
5.7%
2 16090
 
5.1%
7 15491
 
4.9%
6 14274
 
4.5%
3 14186
 
4.5%
8 11677
 
3.7%
5 9888
 
3.1%
Other values (66) 77167
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 317472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 58539
18.4%
1 52061
16.4%
. 30054
 
9.5%
/ 18045
 
5.7%
2 16090
 
5.1%
7 15491
 
4.9%
6 14274
 
4.5%
3 14186
 
4.5%
8 11677
 
3.7%
5 9888
 
3.1%
Other values (66) 77167
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 317472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 58539
18.4%
1 52061
16.4%
. 30054
 
9.5%
/ 18045
 
5.7%
2 16090
 
5.1%
7 15491
 
4.9%
6 14274
 
4.5%
3 14186
 
4.5%
8 11677
 
3.7%
5 9888
 
3.1%
Other values (66) 77167
24.3%

report-no
Text

Missing 

Distinct1886
Distinct (%)99.6%
Missing18106
Missing (%)90.5%
Memory size692.4 KiB
2025-07-03T16:08:37.238598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

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Median length91
Mean length19.375396
Min length4

Characters and Unicode

Total characters36,697
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1,881 ?
Unique (%)99.3%

Sample

1st rowANL-HEP-PR-07-12
2nd rowIGPG-07/03-2
3rd rowLA-UR-07-2051, LLNL-JRNL-410358
4th rowBABAR-PUB-07/015, SLAC-PUB-12417
5th rowPublished in Systemics of Emergence. Research and Development, Minati G., Pessa E., Abram M., Springer, 2006, pages 723-734
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1.7%
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1.3%
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0.7%
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0.6%
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0.4%
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0.4%
clns 13
 
0.4%
cleo 13
 
0.4%
university 12
 
0.4%
mit-ctp 10
 
0.3%
Other values (2742) 3032
93.3%
2025-07-03T16:08:37.387819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4483
 
12.2%
0 3740
 
10.2%
7 2260
 
6.2%
P 1693
 
4.6%
2 1476
 
4.0%
1368
 
3.7%
1 1325
 
3.6%
T 1256
 
3.4%
/ 1180
 
3.2%
S 1165
 
3.2%
Other values (73) 16751
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 4483
 
12.2%
0 3740
 
10.2%
7 2260
 
6.2%
P 1693
 
4.6%
2 1476
 
4.0%
1368
 
3.7%
1 1325
 
3.6%
T 1256
 
3.4%
/ 1180
 
3.2%
S 1165
 
3.2%
Other values (73) 16751
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 4483
 
12.2%
0 3740
 
10.2%
7 2260
 
6.2%
P 1693
 
4.6%
2 1476
 
4.0%
1368
 
3.7%
1 1325
 
3.6%
T 1256
 
3.4%
/ 1180
 
3.2%
S 1165
 
3.2%
Other values (73) 16751
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 4483
 
12.2%
0 3740
 
10.2%
7 2260
 
6.2%
P 1693
 
4.6%
2 1476
 
4.0%
1368
 
3.7%
1 1325
 
3.6%
T 1256
 
3.4%
/ 1180
 
3.2%
S 1165
 
3.2%
Other values (73) 16751
45.6%
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Missing0
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Memory size1.2 MiB
2025-07-03T16:08:37.483761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length94
Mean length14.08775
Min length5

Characters and Unicode

Total characters281,755
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1,323 ?
Unique (%)6.6%

Sample

1st rowhep-ph
2nd rowmath.CO cs.CG
3rd rowphysics.gen-ph
4th rowmath.CO
5th rowmath.CA math.FA
ValueCountFrequency (%)
astro-ph 4044
 
13.4%
hep-th 1907
 
6.3%
hep-ph 1882
 
6.3%
quant-ph 1342
 
4.5%
gr-qc 1109
 
3.7%
cond-mat.mtrl-sci 930
 
3.1%
cond-mat.str-el 883
 
2.9%
cond-mat.stat-mech 881
 
2.9%
math-ph 870
 
2.9%
math.mp 870
 
2.9%
Other values (134) 15356
51.1%
2025-07-03T16:08:37.636053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 29176
 
10.4%
t 27492
 
9.8%
- 24788
 
8.8%
a 21981
 
7.8%
. 17385
 
6.2%
p 17243
 
6.1%
m 16648
 
5.9%
s 15630
 
5.5%
c 13882
 
4.9%
o 12359
 
4.4%
Other values (35) 85171
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 29176
 
10.4%
t 27492
 
9.8%
- 24788
 
8.8%
a 21981
 
7.8%
. 17385
 
6.2%
p 17243
 
6.1%
m 16648
 
5.9%
s 15630
 
5.5%
c 13882
 
4.9%
o 12359
 
4.4%
Other values (35) 85171
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 29176
 
10.4%
t 27492
 
9.8%
- 24788
 
8.8%
a 21981
 
7.8%
. 17385
 
6.2%
p 17243
 
6.1%
m 16648
 
5.9%
s 15630
 
5.5%
c 13882
 
4.9%
o 12359
 
4.4%
Other values (35) 85171
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 29176
 
10.4%
t 27492
 
9.8%
- 24788
 
8.8%
a 21981
 
7.8%
. 17385
 
6.2%
p 17243
 
6.1%
m 16648
 
5.9%
s 15630
 
5.5%
c 13882
 
4.9%
o 12359
 
4.4%
Other values (35) 85171
30.2%

license
URL

Missing 

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Missing18504
Missing (%)92.5%
Memory size724.4 KiB
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
 
1472
http://creativecommons.org/licenses/by/4.0/
 
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5
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3
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3
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4
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http://arxiv.org/licenses/nonexclusive-distrib/1.0/ 1472
 
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http://creativecommons.org/licenses/by-nc-sa/3.0/ 5
 
< 0.1%
http://creativecommons.org/licenses/publicdomain/ 3
 
< 0.1%
http://creativecommons.org/licenses/by/3.0/ 3
 
< 0.1%
http://creativecommons.org/licenses/by-nc-nd/4.0/ 2
 
< 0.1%
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http://creativecommons.org/licenses/by-nc-sa/4.0/ 1
 
< 0.1%
(Missing) 18504
92.5%
ValueCountFrequency (%)
http 1496
 
7.5%
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92.5%
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arxiv.org 1472
 
7.4%
creativecommons.org 24
 
0.1%
(Missing) 18504
92.5%
ValueCountFrequency (%)
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7.4%
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< 0.1%
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< 0.1%
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< 0.1%
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< 0.1%
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< 0.1%
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(Missing) 18504
92.5%
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(Missing) 18504
92.5%
ValueCountFrequency (%)
1496
 
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(Missing) 18504
92.5%
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Memory size16.2 MiB
2025-07-03T16:08:37.778387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2,034
Median length1,390
Mean length801.3511
Min length20

Characters and Unicode

Total characters16,027,022
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19,966 ?
Unique (%)99.8%

Sample

1st row A fully differential calculation in perturbative quantum chromodynamics is presented for the production of massive photon pairs at hadron colliders. All next-to-leading order perturbative contributions from quark-antiquark, gluon-(anti)quark, and gluon-gluon subprocesses are included, as well as all-orders resummation of initial-state gluon radiation valid at next-to-next-to-leading logarithmic accuracy. The region of phase space is specified in which the calculation is most reliable. Good agreement is demonstrated with data from the Fermilab Tevatron, and predictions are made for more detailed tests with CDF and DO data. Predictions are shown for distributions of diphoton pairs produced at the energy of the Large Hadron Collider (LHC). Distributions of the diphoton pairs from the decay of a Higgs boson are contrasted with those produced from QCD processes at the LHC, showing that enhanced sensitivity to the signal can be obtained with judicious selection of events.
2nd row We describe a new algorithm, the $(k,\ell)$-pebble game with colors, and use it obtain a characterization of the family of $(k,\ell)$-sparse graphs and algorithmic solutions to a family of problems concerning tree decompositions of graphs. Special instances of sparse graphs appear in rigidity theory and have received increased attention in recent years. In particular, our colored pebbles generalize and strengthen the previous results of Lee and Streinu and give a new proof of the Tutte-Nash-Williams characterization of arboricity. We also present a new decomposition that certifies sparsity based on the $(k,\ell)$-pebble game with colors. Our work also exposes connections between pebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and Westermann and Hendrickson.
3rd row The evolution of Earth-Moon system is described by the dark matter field fluid model proposed in the Meeting of Division of Particle and Field 2004, American Physical Society. The current behavior of the Earth-Moon system agrees with this model very well and the general pattern of the evolution of the Moon-Earth system described by this model agrees with geological and fossil evidence. The closest distance of the Moon to Earth was about 259000 km at 4.5 billion years ago, which is far beyond the Roche's limit. The result suggests that the tidal friction may not be the primary cause for the evolution of the Earth-Moon system. The average dark matter field fluid constant derived from Earth-Moon system data is 4.39 x 10^(-22) s^(-1)m^(-1). This model predicts that the Mars's rotation is also slowing with the angular acceleration rate about -4.38 x 10^(-22) rad s^(-2).
4th row We show that a determinant of Stirling cycle numbers counts unlabeled acyclic single-source automata. The proof involves a bijection from these automata to certain marked lattice paths and a sign-reversing involution to evaluate the determinant.
5th row In this paper we show how to compute the $\Lambda_{\alpha}$ norm, $\alpha\ge 0$, using the dyadic grid. This result is a consequence of the description of the Hardy spaces $H^p(R^N)$ in terms of dyadic and special atoms.
ValueCountFrequency (%)
the 196264
 
8.1%
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4.8%
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2.6%
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2.4%
in 54266
 
2.2%
to 45635
 
1.9%
we 37739
 
1.6%
is 36940
 
1.5%
for 28038
 
1.2%
that 25434
 
1.0%
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2025-07-03T16:08:37.974285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

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2252000
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6.4%
a 1011134
 
6.3%
o 950892
 
5.9%
n 922098
 
5.8%
s 854614
 
5.3%
r 810388
 
5.1%
l 535188
 
3.3%
Other values (86) 4953010
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16027022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2252000
14.1%
e 1553229
 
9.7%
t 1166718
 
7.3%
i 1017751
 
6.4%
a 1011134
 
6.3%
o 950892
 
5.9%
n 922098
 
5.8%
s 854614
 
5.3%
r 810388
 
5.1%
l 535188
 
3.3%
Other values (86) 4953010
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16027022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2252000
14.1%
e 1553229
 
9.7%
t 1166718
 
7.3%
i 1017751
 
6.4%
a 1011134
 
6.3%
o 950892
 
5.9%
n 922098
 
5.8%
s 854614
 
5.3%
r 810388
 
5.1%
l 535188
 
3.3%
Other values (86) 4953010
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16027022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2252000
14.1%
e 1553229
 
9.7%
t 1166718
 
7.3%
i 1017751
 
6.4%
a 1011134
 
6.3%
o 950892
 
5.9%
n 922098
 
5.8%
s 854614
 
5.3%
r 810388
 
5.1%
l 535188
 
3.3%
Other values (86) 4953010
30.9%

versions
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.8 MiB
Distinct1875
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Minimum2007-05-23 00:00:00
Maximum2025-06-16 00:00:00
Invalid dates0
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2025-07-03T16:08:38.024781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-03T16:08:38.071094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

authors_parsed
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size2.0 MiB

Interactions

2025-07-03T16:08:35.109182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-07-03T16:08:35.178324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-03T16:08:35.245045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-03T16:08:35.345504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idsubmitterauthorstitlecommentsjournal-refdoireport-nocategorieslicenseabstractversionsupdate_dateauthors_parsed
0704.0001Pavel NadolskyC. Bal\'azs, E. L. Berger, P. M. Nadolsky, C.-P. YuanCalculation of prompt diphoton production cross sections at Tevatron and\n LHC energies37 pages, 15 figures; published versionPhys.Rev.D76:013009,200710.1103/PhysRevD.76.013009ANL-HEP-PR-07-12hep-phNoneA fully differential calculation in perturbative quantum chromodynamics is\npresented for the production of massive photon pairs at hadron colliders. All\nnext-to-leading order perturbative contributions from quark-antiquark,\ngluon-(anti)quark, and gluon-gluon subprocesses are included, as well as\nall-orders resummation of initial-state gluon radiation valid at\nnext-to-next-to-leading logarithmic accuracy. The region of phase space is\nspecified in which the calculation is most reliable. Good agreement is\ndemonstrated with data from the Fermilab Tevatron, and predictions are made for\nmore detailed tests with CDF and DO data. Predictions are shown for\ndistributions of diphoton pairs produced at the energy of the Large Hadron\nCollider (LHC). Distributions of the diphoton pairs from the decay of a Higgs\nboson are contrasted with those produced from QCD processes at the LHC, showing\nthat enhanced sensitivity to the signal can be obtained with judicious\nselection of events.\n[{'version': 'v1', 'created': 'Mon, 2 Apr 2007 19:18:42 GMT'}, {'version': 'v2', 'created': 'Tue, 24 Jul 2007 20:10:27 GMT'}]2008-11-26[[Balázs, C., ], [Berger, E. L., ], [Nadolsky, P. M., ], [Yuan, C. -P., ]]
1704.0002Louis TheranIleana Streinu and Louis TheranSparsity-certifying Graph DecompositionsTo appear in Graphs and CombinatoricsNoneNoneNonemath.CO cs.CGhttp://arxiv.org/licenses/nonexclusive-distrib/1.0/We describe a new algorithm, the $(k,\ell)$-pebble game with colors, and use\nit obtain a characterization of the family of $(k,\ell)$-sparse graphs and\nalgorithmic solutions to a family of problems concerning tree decompositions of\ngraphs. Special instances of sparse graphs appear in rigidity theory and have\nreceived increased attention in recent years. In particular, our colored\npebbles generalize and strengthen the previous results of Lee and Streinu and\ngive a new proof of the Tutte-Nash-Williams characterization of arboricity. We\nalso present a new decomposition that certifies sparsity based on the\n$(k,\ell)$-pebble game with colors. Our work also exposes connections between\npebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and\nWestermann and Hendrickson.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 02:26:18 GMT'}, {'version': 'v2', 'created': 'Sat, 13 Dec 2008 17:26:00 GMT'}]2008-12-13[[Streinu, Ileana, ], [Theran, Louis, ]]
2704.0003Hongjun PanHongjun PanThe evolution of the Earth-Moon system based on the dark matter field\n fluid model23 pages, 3 figuresNoneNoneNonephysics.gen-phNoneThe evolution of Earth-Moon system is described by the dark matter field\nfluid model proposed in the Meeting of Division of Particle and Field 2004,\nAmerican Physical Society. The current behavior of the Earth-Moon system agrees\nwith this model very well and the general pattern of the evolution of the\nMoon-Earth system described by this model agrees with geological and fossil\nevidence. The closest distance of the Moon to Earth was about 259000 km at 4.5\nbillion years ago, which is far beyond the Roche's limit. The result suggests\nthat the tidal friction may not be the primary cause for the evolution of the\nEarth-Moon system. The average dark matter field fluid constant derived from\nEarth-Moon system data is 4.39 x 10^(-22) s^(-1)m^(-1). This model predicts\nthat the Mars's rotation is also slowing with the angular acceleration rate\nabout -4.38 x 10^(-22) rad s^(-2).\n[{'version': 'v1', 'created': 'Sun, 1 Apr 2007 20:46:54 GMT'}, {'version': 'v2', 'created': 'Sat, 8 Dec 2007 23:47:24 GMT'}, {'version': 'v3', 'created': 'Sun, 13 Jan 2008 00:36:28 GMT'}]2008-01-13[[Pan, Hongjun, ]]
3704.0004David CallanDavid CallanA determinant of Stirling cycle numbers counts unlabeled acyclic\n single-source automata11 pagesNoneNoneNonemath.CONoneWe show that a determinant of Stirling cycle numbers counts unlabeled acyclic\nsingle-source automata. The proof involves a bijection from these automata to\ncertain marked lattice paths and a sign-reversing involution to evaluate the\ndeterminant.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 03:16:14 GMT'}]2007-05-23[[Callan, David, ]]
4704.0005Alberto TorchinskyWael Abu-Shammala and Alberto TorchinskyFrom dyadic $\Lambda_{\alpha}$ to $\Lambda_{\alpha}$NoneIllinois J. Math. 52 (2008) no.2, 681-689NoneNonemath.CA math.FANoneIn this paper we show how to compute the $\Lambda_{\alpha}$ norm, $\alpha\ge\n0$, using the dyadic grid. This result is a consequence of the description of\nthe Hardy spaces $H^p(R^N)$ in terms of dyadic and special atoms.\n[{'version': 'v1', 'created': 'Mon, 2 Apr 2007 18:09:58 GMT'}]2013-10-15[[Abu-Shammala, Wael, ], [Torchinsky, Alberto, ]]
5704.0006Yue Hin PongY. H. Pong and C. K. LawBosonic characters of atomic Cooper pairs across resonance6 pages, 4 figures, accepted by PRANone10.1103/PhysRevA.75.043613Nonecond-mat.mes-hallNoneWe study the two-particle wave function of paired atoms in a Fermi gas with\ntunable interaction strengths controlled by Feshbach resonance. The Cooper pair\nwave function is examined for its bosonic characters, which is quantified by\nthe correction of Bose enhancement factor associated with the creation and\nannihilation composite particle operators. An example is given for a\nthree-dimensional uniform gas. Two definitions of Cooper pair wave function are\nexamined. One of which is chosen to reflect the off-diagonal long range order\n(ODLRO). Another one corresponds to a pair projection of a BCS state. On the\nside with negative scattering length, we found that paired atoms described by\nODLRO are more bosonic than the pair projected definition. It is also found\nthat at $(k_F a)^{-1} \ge 1$, both definitions give similar results, where more\nthan 90% of the atoms occupy the corresponding molecular condensates.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 04:24:59 GMT'}]2015-05-13[[Pong, Y. H., ], [Law, C. K., ]]
6704.0007Alejandro CorichiAlejandro Corichi, Tatjana Vukasinac and Jose A. ZapataPolymer Quantum Mechanics and its Continuum Limit16 pages, no figures. Typos corrected to match published versionPhys.Rev.D76:044016,200710.1103/PhysRevD.76.044016IGPG-07/03-2gr-qcNoneA rather non-standard quantum representation of the canonical commutation\nrelations of quantum mechanics systems, known as the polymer representation has\ngained some attention in recent years, due to its possible relation with Planck\nscale physics. In particular, this approach has been followed in a symmetric\nsector of loop quantum gravity known as loop quantum cosmology. Here we explore\ndifferent aspects of the relation between the ordinary Schroedinger theory and\nthe polymer description. The paper has two parts. In the first one, we derive\nthe polymer quantum mechanics starting from the ordinary Schroedinger theory\nand show that the polymer description arises as an appropriate limit. In the\nsecond part we consider the continuum limit of this theory, namely, the reverse\nprocess in which one starts from the discrete theory and tries to recover back\nthe ordinary Schroedinger quantum mechanics. We consider several examples of\ninterest, including the harmonic oscillator, the free particle and a simple\ncosmological model.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 04:27:22 GMT'}, {'version': 'v2', 'created': 'Wed, 22 Aug 2007 22:42:11 GMT'}]2008-11-26[[Corichi, Alejandro, ], [Vukasinac, Tatjana, ], [Zapata, Jose A., ]]
7704.0008Damian SwiftDamian C. SwiftNumerical solution of shock and ramp compression for general material\n propertiesMinor correctionsJournal of Applied Physics, vol 104, 073536 (2008)10.1063/1.2975338LA-UR-07-2051, LLNL-JRNL-410358cond-mat.mtrl-scihttp://arxiv.org/licenses/nonexclusive-distrib/1.0/A general formulation was developed to represent material models for\napplications in dynamic loading. Numerical methods were devised to calculate\nresponse to shock and ramp compression, and ramp decompression, generalizing\nprevious solutions for scalar equations of state. The numerical methods were\nfound to be flexible and robust, and matched analytic results to a high\naccuracy. The basic ramp and shock solution methods were coupled to solve for\ncomposite deformation paths, such as shock-induced impacts, and shock\ninteractions with a planar interface between different materials. These\ncalculations capture much of the physics of typical material dynamics\nexperiments, without requiring spatially-resolving simulations. Example\ncalculations were made of loading histories in metals, illustrating the effects\nof plastic work on the temperatures induced in quasi-isentropic and\nshock-release experiments, and the effect of a phase transition.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 04:47:20 GMT'}, {'version': 'v2', 'created': 'Thu, 10 Apr 2008 08:42:28 GMT'}, {'version': 'v3', 'created': 'Tue, 1 Jul 2008 18:54:28 GMT'}]2009-02-05[[Swift, Damian C., ]]
8704.0009Paul HarveyPaul Harvey, Bruno Merin, Tracy L. Huard, Luisa M. Rebull, Nicholas\n Chapman, Neal J. Evans II, Philip C. MyersThe Spitzer c2d Survey of Large, Nearby, Insterstellar Clouds. IX. The\n Serpens YSO Population As Observed With IRAC and MIPSNoneAstrophys.J.663:1149-1173,200710.1086/518646Noneastro-phNoneWe discuss the results from the combined IRAC and MIPS c2d Spitzer Legacy\nobservations of the Serpens star-forming region. In particular we present a set\nof criteria for isolating bona fide young stellar objects, YSO's, from the\nextensive background contamination by extra-galactic objects. We then discuss\nthe properties of the resulting high confidence set of YSO's. We find 235 such\nobjects in the 0.85 deg^2 field that was covered with both IRAC and MIPS. An\nadditional set of 51 lower confidence YSO's outside this area is identified\nfrom the MIPS data combined with 2MASS photometry. We describe two sets of\nresults, color-color diagrams to compare our observed source properties with\nthose of theoretical models for star/disk/envelope systems and our own modeling\nof the subset of our objects that appear to be star+disks. These objects\nexhibit a very wide range of disk properties, from many that can be fit with\nactively accreting disks to some with both passive disks and even possibly\ndebris disks. We find that the luminosity function of YSO's in Serpens extends\ndown to at least a few x .001 Lsun or lower for an assumed distance of 260 pc.\nThe lower limit may be set by our inability to distinguish YSO's from\nextra-galactic sources more than by the lack of YSO's at very low luminosities.\nA spatial clustering analysis shows that the nominally less-evolved YSO's are\nmore highly clustered than the later stages and that the background\nextra-galactic population can be fit by the same two-point correlation function\nas seen in other extra-galactic studies. We also present a table of matches\nbetween several previous infrared and X-ray studies of the Serpens YSO\npopulation and our Spitzer data set.\n[{'version': 'v1', 'created': 'Mon, 2 Apr 2007 19:41:34 GMT'}]2010-03-18[[Harvey, Paul, ], [Merin, Bruno, ], [Huard, Tracy L., ], [Rebull, Luisa M., ], [Chapman, Nicholas, ], [Evans, Neal J., II], [Myers, Philip C., ]]
9704.0010Sergei OvchinnikovSergei OvchinnikovPartial cubes: structures, characterizations, and constructions36 pages, 17 figuresNoneNoneNonemath.CONonePartial cubes are isometric subgraphs of hypercubes. Structures on a graph\ndefined by means of semicubes, and Djokovi\'{c}'s and Winkler's relations play\nan important role in the theory of partial cubes. These structures are employed\nin the paper to characterize bipartite graphs and partial cubes of arbitrary\ndimension. New characterizations are established and new proofs of some known\nresults are given.\n The operations of Cartesian product and pasting, and expansion and\ncontraction processes are utilized in the paper to construct new partial cubes\nfrom old ones. In particular, the isometric and lattice dimensions of finite\npartial cubes obtained by means of these operations are calculated.\n[{'version': 'v1', 'created': 'Sat, 31 Mar 2007 05:10:16 GMT'}]2007-05-23[[Ovchinnikov, Sergei, ]]
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19990708.2139R. RajeshColm Connaughton, R. Rajesh, Oleg ZaboronskiConstant flux relation for aggregation models with desorption and\n fragmentation5 pages 2 figures, To appear in Physica APhysica A Vol. 384, pg 108 (2007).10.1016/j.physa.2007.04.074Nonecond-mat.stat-mechNoneWe study mass fluxes in aggregation models where mass transfer to large\nscales by aggregation occurs alongside desorption or fragmentation. Two models\nare considered. (1) A system of diffusing, aggregating particles with influx\nand outflux of particles (in-out model) (2) A system of diffusing aggregating\nparticles with fragmentation (chipping model). Both these models can exist in\nphases where probability distributions are power laws. In these power law\nphases, we argue that the two point correlation function should have a certain\nhomogeneity exponent. These arguments are based on the exact constant flux\nscaling valid for simple aggregation with input. Predictions are compared with\nMonte Carlo simulations.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 05:45:23 GMT'}]2009-11-13[[Connaughton, Colm, ], [Rajesh, R., ], [Zaboronski, Oleg, ]]
19991708.2140Zorica KonstantinovicZ Konstantinovic, M Garcia del Muro, X Batlle, and A Labarta, M.\n VarelaThe nanostructural origin of the ac conductance in dielectric granular\n metals: the case study of Co_20(ZrO_2)_80Available online at:\n http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=APPLAB000091000005052108000001&idtype=cvips&gifs=yesAppl. Phys. Lett. 91, 052108 (2007)10.1063/1.2766858Nonecond-mat.mtrl-sci cond-mat.dis-nnNoneWe show which is the nanostructure required in granular Co20(ZrO2)80 thin\nfilms to produce an ac response such as the one that is universally observed in\na very wide variety of dielectric materials. A bimodal size distribution of Co\nparticles yields randomly competing conductance channels which allow both\nthermally assisted tunneling through small particles and capacitive conductance\namong larger particles that are further apart. A model consisting on a simple\ncubic random resistance-capacitor network describes quantitatively the\nexperimental results as functions of temperature and frequency, and enables the\ndetermination of the microscopic parameters controlling the ac response of the\nsamples.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 06:32:51 GMT'}]2007-08-17[[Konstantinovic, Z, ], [del Muro, M Garcia, ], [Batlle, X, ], [Labarta, A, ], [Varela, M., ]]
19992708.2141Shyamal LakshminarayananShyamal LakshminarayananA model for exploring bird morphology7 pages, 1 table, 3 figuresNoneNoneNoneq-bio.OTNoneA simplified model of the bird skeleton along with elongation parameters for\nthe flight feathers is used to explore the diversity of bird shapes. Varying a\nsmall number of parameters simulates a wide range of observed bird silhouettes.\nThe model may serve to examine developmental factors involved, help museum\ncurators develop computational approaches to bird morphometry and has\napplications in computer generated illustration.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 06:34:53 GMT'}]2007-08-17[[Lakshminarayanan, Shyamal, ]]
19993708.2142Minhsiu HsiehMin-Hsiu Hsieh, Igor Devetak and Todd BrunGeneral entanglement-assisted quantum error-correcting codes7 pages, no figurePhys. Rev. A 76, 062313 (2007)10.1103/PhysRevA.76.062313Nonequant-phNoneEntanglement-assisted quantum error-correcting codes (EAQECCs) make use of\npre-existing entanglement between the sender and receiver to boost the rate of\ntransmission. It is possible to construct an EAQECC from any classical linear\ncode, unlike standard QECCs which can only be constructed from dual-containing\ncodes. Operator quantum error-correcting codes (OQECCs) allow certain errors to\nbe corrected (or prevented) passively, reducing the complexity of the\ncorrection procedure. We combine these two extensions of standard quantum error\ncorrection into a unified entanglement-assisted quantum error correction\nformalism. This new scheme, which we call entanglement-assisted operator\nquantum error correction (EAOQEC), is the most general and powerful quantum\nerror-correcting technique known, retaining the advantages of both\nentanglement-assistance and passive correction. We present the formalism, show\nthe considerable freedom in constructing EAOQECCs from classical codes, and\ndemonstrate the construction with examples.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 06:40:58 GMT'}]2009-11-13[[Hsieh, Min-Hsiu, ], [Devetak, Igor, ], [Brun, Todd, ]]
19994708.2143Yuan XuYuan Xu, Shuai Wang and Ke XiaSpin-transfer torques in anti-ferromagnetic metals from first principlesThe paper has substantially been rewritten, 4 pages, 5 figuresPhysical Review Letters, 100, 226602 (2008)10.1103/PhysRevLett.100.226602Nonecond-mat.mtrl-sciNoneIn spite of the absence of a macroscopic magnetic moment, an anti-ferromagnet\nis spin-polarized on an atomic scale. The electric current passing through a\nconducting anti-ferromagnet is polarized as well, leading to spin-transfer\ntorques when the order parameter is textured, such as in anti-ferromagnetic\nnon-collinear spin valves and domain walls. We report a first principles study\non the electronic transport properties of anti-ferromagnetic systems. The\ncurrent-induced spin torques acting on the magnetic moments are comparable with\nthose in conventional ferromagnetic materials, leading to measurable angular\nresistances and current-induced magnetization dynamics. In contrast to\nferromagnets, spin torques in anti-ferromagnets are very nonlocal. The torques\nacting far away from the center of an anti-ferromagnetic domain wall should\nfacilitate current-induced domain wall motion.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 07:55:58 GMT'}, {'version': 'v2', 'created': 'Wed, 4 Jun 2008 02:22:57 GMT'}]2009-09-29[[Xu, Yuan, ], [Wang, Shuai, ], [Xia, Ke, ]]
19995708.2144Eric WoolgarE. WoolgarSome Applications of Ricci Flow in PhysicsMinor corrections in Sections IV and VI. Invited talk at Theory\n Canada III meeting, June 2007; submitted to Proceedings. Dedicated to Rafael\n D Sorkin on the occasion of his 60th birthdayCan.J.Phys.86:645,200810.1139/P07-146Nonehep-th gr-qc math.DGNoneI discuss certain applications of the Ricci flow in physics. I first review\nhow it arises in the renormalization group (RG) flow of a nonlinear sigma\nmodel. I then review the concept of a Ricci soliton and recall how a soliton\nwas used to discuss the RG flow of mass in 2-dimensions. I then present recent\nresults obtained with Oliynyk on the flow of mass in higher dimensions. The\nfinal section discusses one way in which Ricci flow may arise in general\nrelativity, particularly for static metrics.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 06:50:14 GMT'}, {'version': 'v2', 'created': 'Mon, 27 Aug 2007 04:52:23 GMT'}, {'version': 'v3', 'created': 'Mon, 31 Dec 2007 04:03:04 GMT'}]2009-11-13[[Woolgar, E., ]]
19996708.2145John BulavaJohn Bulava, Robert Edwards, George Fleming, K. Jimmy Juge, Adam C.\n Lichtl, Nilmani Mathur, Colin Morningstar, David Richards, Stephen J. WallaceResults and Frontiers in Lattice Baryon SpectroscopyTo appear in the proceedings for the VII Latin American Symposium of\n Nuclear Physics and ApplicationsAIP Conf.Proc.947:137-140,200710.1063/1.2813791Nonehep-latNoneThe Lattice Hadron Physics Collaboration (LHPC) baryon spectroscopy effort is\nreviewed. To date the LHPC has performed exploratory Lattice QCD calculations\nof the low-lying spectrum of Nucleon and Delta baryons. These calculations\ndemonstrate the effectiveness of our method by obtaining the masses of an\nunprecedented number of excited states with definite quantum numbers. Future\nwork of the project is outlined.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 07:03:27 GMT'}]2014-11-18[[Bulava, John, ], [Edwards, Robert, ], [Fleming, George, ], [Juge, K. Jimmy, ], [Lichtl, Adam C., ], [Mathur, Nilmani, ], [Morningstar, Colin, ], [Richards, David, ], [Wallace, Stephen J., ]]
19997708.2146Kaptari LeonidS. M. Dorkin (International University Dubna, Dubna), M. Beyer (Inst.\n of Phys. Univ. of Rostock), S. S. Semikh and L. P. Kaptari (Bogoliubov Lab.\n Theor. Phys., JINR, Dubna)Two-Fermion Bound States within the Bethe-Salpeter Approach32 pages, XIII Tables, 8 figuresFewBodySyst.42:1-32,200810.1007/s00601-008-0196-8Nonenucl-th hep-phNoneTo solve the spinor-spinor Bethe-Salpeter equation in Euclidean space we\npropose a novel method related to the use of hyperspherical harmonics. We\nsuggest an appropriate extension to form a new basis of spin-angular harmonics\nthat is suitable for a representation of the vertex functions. We present a\nnumerical algorithm to solve the Bethe-Salpeter equation and investigate in\ndetail the properties of the solution for the scalar, pseudoscalar and vector\nmeson exchange kernels including the stability of bound states. We also compare\nour results to the non relativistic ones and to the results given by light\nfront dynamics.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 07:08:20 GMT'}]2008-11-26[[Dorkin, S. M., , International University Dubna, Dubna], [Beyer, M., , Inst.\n of Phys. Univ. of Rostock], [Semikh, S. S., , Bogoliubov Lab.\n Theor. Phys., JINR, Dubna], [Kaptari, L. P., , Bogoliubov Lab.\n Theor. Phys., JINR, Dubna]]
19998708.2147Dalius BalciunasDalius BalciunasThe logistic equation and a critique of the theory of natural selection31 pages, 5 figures, appendixNoneNoneNoneq-bio.PENoneSpecies coexistence is one of the central themes in modern ecology.\nCoexistence is a prerequisite of biological diversity. However, the question\narises how biodiversity can be reconciled with the statement of competition\ntheory, which asserts that competing species cannot coexist. To solve this\nproblem natural selection theory is rejected because it contradicts kinetic\nmodels of interacting populations. Biological evolution is presented as a\nprocess equivalent to a chemical reaction. The main point is that interactions\noccur between self-replicating units. Under these assumptions biodiversity is\npossible if and only if species are identical with respect to the patterns of\nenergy flow in which individuals are involved.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 07:15:04 GMT'}]2007-08-17[[Balciunas, Dalius, ]]
19999708.2148Reza NourafkanR. Nourafkan and N. NafariKondo lattice model at half-filling11 pages, 5 figuresNone10.1088/0953-8984/20/25/255231Nonecond-mat.str-elNoneThe single- and two-channel Kondo lattice model consisting of localized spins\ninteracting antiferromagnetically with the itinerent electrons, are studied\nusing dynamical mean field theory. As an impurity solver for the effective\nsingle impurity Anderson model we used the exact diagonalization (ED) method.\nUsing ED allowed us to perform calculations for low temperatures and couplings\nof arbitrary large strength. Our results for the single-channel case confirm\nand extend the recent investigations. In the two-channel case we find a\nsymmetry breaking phase transition with increasing coupling strength.\n[{'version': 'v1', 'created': 'Thu, 16 Aug 2007 07:21:03 GMT'}]2009-11-13[[Nourafkan, R., ], [Nafari, N., ]]